System and method for real-time whiteboard capture and processing

ABSTRACT

A system that captures both whiteboard content and audio signals of a meeting using a video camera and records or transmits them in real-time. The Real-Time Whiteboard Capture captures pen strokes on whiteboards in real time using an off-the-shelf video camera. Unlike many existing tools, the RTWCS does not instrument the pens or the whiteboard. It analyzes the sequence of captured video images in real time, classifies the pixels into whiteboard background, pen strokes and foreground objects (e.g., people in front of the whiteboard), and extracts newly written pen strokes. This allows the RTWCS to transmit whiteboard contents using very low bandwidth to remote meeting participants. Combined with other teleconferencing tools such as voice conference and application sharing, the RTWCS becomes a powerful tool to share ideas during online meetings.

This application claims priority under 35 U.S.C. Section 119(e)(1) ofprovisional application No. 60/520,887, filed Nov. 18, 2003.

BACKGROUND

1. Technical Field

This invention is directed toward a system and method for capturing andtransmitting meeting content. More particularly, this invention isdirected towards a system and method for capturing and/or transmittingthe whiteboard content in real-time.

2. Background Art

Meetings constitute a large part of many workers' working time. Makingmore efficient use of this time spent in meetings translates into a bigincrease in productivity.

Although whiteboard sessions are frequent for knowledge workers, theyare not perfect. The content on the board is hard to archive or sharewith others who are not present in the session. People are often busycopying the whiteboard content to their notepads when they should spendtime sharing and absorbing ideas. Sometimes they put a “Do Not Erase”sign on the whiteboard and hope to come back and deal with it later. Inmany cases, they forget or the content is accidentally erased by otherpeople. Furthermore, meeting participants who are on a conference callat remote locations are not able to see the whiteboard content as thelocal participants do. In order to enable this, the meeting sites oftenmust be linked with expensive video conferencing equipment. Suchequipment typically includes a pan-tilt-zoom camera which can becontrolled by the remote participants. This configuration is still notalways satisfactory because the viewing angle, lighting variation, andimage resolution are often inadequate. The lack of functionality ofeffective archiving and indexing of whiteboard contents is alsoproblematic.

Many technologies exist to capture the whiteboard content automatically.One of the earliest, the whiteboard copier, is a special whiteboard witha built-in copier. With a click of a button, the whiteboard content isscanned and printed. Once the whiteboard content is on paper, it can bephotocopied, faxed, put away in the file cabinet, or scanned intodigital form. Recent technologies all attempt to capture the whiteboardin digital form from the start. They generally fall into twocategories—image capture devices and pen tracking devices.

The devices in the first category capture images of the whiteboarddirectly. NTSC-resolution video cameras are often used because of theirlow cost. Since they usually do not have enough resolution for a typicalconference room size whiteboard, several video frames must be stitchedtogether to create a single whiteboard image. The ZombieBoard system[10], deployed internally at Xerox's Palo Alto Research Center, uses aPan-Tilt video camera to scan the whiteboard. The Hawkeye system fromSmartTech opts for a three-camera array that takes imagessimultaneously. Another device in this category is the digital stillcamera. As high resolution digital cameras get cheaper, taking snapshotsof the board with a digital camera has become a popular choice. Toclean-up the results, people use software to crop the non-whiteboardregion and color-balance the images.

There are several disadvantages of the aforementioned image capturedevices, however. For example, they capture the whiteboard content onesnapshot at a time so users have to make a conscious decision to take asnapshot of the whiteboard. Additionally, there is usually a lag betweenwriting on the board and taking a snapshot. Using these devices in realtime teleconferencing scenarios is not very natural or convenient, ifpossible at all.

Devices in the second category track the location of the pen at highfrequency and infer the content of the whiteboard from the history ofthe pen coordinates. Mimio by Virtual Ink Corp. is one of the bestsystems in this category. Mimio is an add-on device attached to the sideof a conventional whiteboard and uses special adaptors for dry-ink pensand an eraser. The adapted pen emits ultrasonic pulses when pressedagainst the board. Two receivers at the add-on device use the differencein time-of-arrival of the audio pulses to triangulate the pencoordinates. Since the history of the pen coordinates is captured, thecontent on the whiteboard can be reconstructed in real time. And becausethe content is captured in vector form, it can be transmitted andarchived with low bandwidth and storage requirements.

Electronic whiteboards also use pen tracking technology. They go onestep further by making the whiteboard an interactive device. Forexample, the SMARTBoard from SmartTech is essentially a computer with agiant touch-sensitive monitor. The user writes on the monitor with aspecial stylus which is tracked by the computer. The computer rendersthe strokes on the screen wherever the stylus touches the screen—as ifthe ink is deposited by the stylus. Because the strokes are computergenerated, it can be edited, re-flowed, and animated.

Pen-tracking devices also have several disadvantages, however.Pen-tracking devices require instrumentation either to the pens anderasers or to the surface that they are writing on. For example, Mimiouses special adapters for dry-ink pens, which make them much thicker andharder to press. Electronic whiteboards are not even compatible with theexisting whiteboards. They use touch screens as their writing surfaces,which limits their install base due to high cost and small size. If thesystem is not turned on or the user writes or erases without using thespecial pens or erasers, the content cannot be recovered by the device.Many people like to use their fingers to correct small mistakes on thewhiteboard. This common behavior causes extra strokes to appear on thecaptured content. Additionally, there is usually minor imprecision inthe tracked pen coordinates, which tends to accumulate and causemis-registrations among the neighboring strokes. Furthermore,pen-tracking devices do not allow multiple users to write on thewhiteboard simultaneously. The image capture devices do not have thisproblem since they work in a What You See Is What You Get (WYSIWYG)manner.

In addition to whiteboard capture devices, much research has been doneon the capture, integration, and access of the multimedia experience.People have developed techniques and systems that use handwritten notes,whiteboard content, slides, or manual annotations to index the recordedvideo and audio for easy access [1, 2, 4, 6, 7, 8, 9, 10, 11, 12, 13,14]. Inspired by those systems, a Whiteboard Capture System (WCS) wasdeveloped [0]. The goal of that project was to build a whiteboardcapture system that combines the benefits of both image capture devicesand pen tracking devices. The key design decision that was made was touse an image capture device as input. Without the requirement forspecial pens and erasers the interaction with the whiteboard is muchmore natural. Furthermore, since this WCS takes images of the whiteboarddirectly, there is no misregistration of the pen strokes. As long as theusers turn on the system before erasing, the content will be preserved.Additionally, images captured with a camera provide much more contextualinformation such as who was writing and which topic was discussed(usually by hand pointing).

Although the WCS can readily and accurately capture key images andassociated audio, its processing is too complex and time consuming tooperate in real-time. Additionally, since a particular digital camerawas chosen as the input device for one embodiment of the WCS for itshigh resolution (4 MP) and the availability of a software developmentkit, which allows one to control the camera from the PC, complex cameracontrol is not necessary. However, because the camera is connected tothe host PC via low bandwidth USB 1.1, the frame rate is limited to 5second per frame. At such a low frame rate, no attempt could be made touse the WCS as a real time conferencing tool. Therefore, the previousWCS was designed to analyze and browse offline meeting recordings. Fromthe input image sequence, a set of key frames that captures the historyof the content on whiteboard and the time stamps associated with eachpen strokes was computed. A key frame contains all the visual contentbefore a major erasure. This information can then be used as a visualindex to the audio meeting recording. Aside from the inability tooperate in real-time, the previous WCS also had to be calibrated everytime it was used or installed.

Therefore, what is needed is a system and method for capturingwhiteboard content in real time that allows the recording and browsingof the content after the meeting, as well as transmission to remotemeeting participants in real time. The system should not requireexpensive specialized video conferencing equipment and should be easy toset up and use. Additionally, transmission to remote meetingparticipants should use as little network bandwidth as possible totransfer the whiteboard content making the system suitable for even dialup network connections.

SUMMARY

The present invention is directed toward a system and process thatrelieves meeting participants from the mundane tasks of note taking bycapturing whiteboard content automatically and communicating thewhiteboard content to remote meeting participants in real time using afraction of the bandwidth required if video conferencing equipment wereused. The Real-Time Whiteboard Capture System (RTWCS) of the presentinvention employs an ordinary whiteboard, not an electronic whiteboard,like some other whiteboard capture systems. Thus, the system can be usedwith any existing whiteboard without modification. The RTWCS also doesnot need to be calibrated when it is set up as it automatically locatesthe whiteboard if it is within its field of view.

The RTWCS employs various techniques that allow it to operate in realtime. First, rather than analyzing the images at pixel level, the RTWCSdivides each video frame into rectangular cells to lower thecomputational cost. Second, the RTWCS's analyzer is structured as apipeline of six analysis modules. If a cell image does not meet thecondition in a particular module, it will not be further processed bythe subsequent modules in the pipeline. Therefore, many cell images donot go through all six modules in the pipeline. At the end, only a smallnumber of cell images containing newly appeared pen strokes come out ofthe analyzer. The third strategy to allow the RTWCS to process data inreal time is specific to the video camera that is used in the RTWCS.This camera (an Aplux MU2 in one embodiment) allows the video frames tobe directly accessed in Bayer format, which is the single channel rawimage captured by the CMOS sensor. In general, a demosaicing algorithmis run on the raw image to produce a RGB color image. By processing thecell images in raw Bayer space instead of red, green, blue (RGB) colorspace and delaying demosaicing until the final step and running it onlyon the cells containing new strokes, the RTWCS saves memory andprocessing by at least 66%. An additional benefit is that one can obtaina higher quality RGB image at the end by using a more sophisticateddemosaicing algorithm than the one built into the camera driver.

The RTWCS keeps track of some states as it processes the input videoframes. These include: 1) The last video frame it has processed; 2) Theage of each cell image in the last frame. The age is defined to be thenumber of frames that the cell image remains unchanged; 3) Cell imageswith whiteboard content that have been detected so far; 4) Thewhiteboard background model.

In one embodiment of the invention, an image frame cell in Bayer formatis input into the RTWCS. Once input, this image frame cell is comparedto the previous image in the corresponding cell location in the previousframe and it is determined if the cell image has changed, preferablyusing a Normalized Cross-correlation algorithm. If the cell image haschanged, the cell age is set to 1 and if it is determined that the cellimage has not changed, the cell age is incremented. An assessment isthen made as to whether its age is greater than a given age threshold.If not, the cell frame image is not processed any further. If the cellage is greater than the age threshold, then the whiteboard background iscomputed for this cell frame image and then this cell frame image isused to update the whiteboard color model. The cell image is thenclassified as either a foreground or whiteboard cell. Any cellsclassified as foreground are no longer processed. If the cell frameimage is a whiteboard cell, any newly appeared strokes are extracted.Optionally, the color of the newly extracted strokes can be enhanced andthe newly extracted strokes can be transmitted to a remote meetingparticipant. This process is repeated for each image frame cell in theframe for each sequential frame. It should be noted that most processingis performed on image data in Bayer format.

Because of white-balancing and color enhancement, the quality of thewhiteboard contents that the remote participant sees is much better thanthat of that would be captured during a meeting using video conferencingequipment.

Besides providing new conferencing functionality in real-time, the RTWCSstill provides for the functionality of the aforementioned WCS. Forexample, from the input image sequence, a set of key frames thatcaptures the history of the content on whiteboard and the time stampsassociated with each pen stroke is computed. This information can thenbe used as a visual index to the audio meeting recording.

DESCRIPTION OF THE DRAWINGS

The file of this patent contains at least one drawing executed in color.Copies of this patent or patent application publication with colordrawing(s) will be provided by the U.S. Patent and Trademark Office uponrequest and payment of the necessary fee.

The specific features, aspects, and advantages of the present inventionwill become better understood with regard to the following description,appended claims, and accompanying drawings where:

FIG. 1 is a diagram depicting a general purpose computing deviceconstituting an exemplary system for implementing the invention.

FIG. 2 is a diagram depicting three main components of the white boardcapture system—the capture unit, analysis server and the browsingsoftware.

FIG. 3 shows selected frames from an 82 second input image sequence.

FIG. 4A is a system diagram depicting the image analysis module of oneembodiment of the system and method according to the invention.

FIG. 4B is the same diagram shown in FIG. 4A captured and color enhancedby the system and method according to the invention.

FIG. 5 is a flow chart depicting the image analysis processing of thesystem and method according to the invention.

FIG. 6 is a flow chart depicting the overall process of analyzing theinput frames.

FIG. 7 is a technique of computing whiteboard color.

FIG. 8 shows a series of images throughout the processing by the systemand method according to the invention: Left—Depicts colors of the cellimages. Note that the strokes on the whiteboard are removed by thebackground color estimation module. Middle—Colors of the cell imagesthat go into the Update Module. Note the black regions contain the cellcolors that are filtered out by both the change detector and the colorestimator. Right—Integrated whiteboard color model.

FIG. 9 is a flow chart depicting the cell classification process of thesystem and method according to the present invention.

FIG. 10 shows selected frames of classification results.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

In the following description of the preferred embodiments of the presentinvention, reference is made to the accompanying drawings that form apart hereof, and in which is shown by way of illustration specificembodiments in which the invention may be practiced. It is understoodthat other embodiments may be utilized and structural changes may bemade without departing from the scope of the present invention.

1.0 Exemplary Operating Environment

FIG. 1 illustrates an example of a suitable computing system environment100 on which the invention may be implemented. The computing systemenvironment 100 is only one example of a suitable computing environmentand is not intended to suggest any limitation as to the scope of use orfunctionality of the invention. Neither should the computing environment100 be interpreted as having any dependency or requirement relating toany one or combination of components illustrated in the exemplaryoperating environment 100.

The invention is operational with numerous other general purpose orspecial purpose computing system environments or configurations.Examples of well known computing systems, environments, and/orconfigurations that may be suitable for use with the invention include,but are not limited to, personal computers, server computers, hand-heldor laptop devices, multiprocessor systems, microprocessor-based systems,set top boxes, programmable consumer electronics, network PCs,minicomputers, mainframe computers, distributed computing environmentsthat include any of the above systems or devices, and the like.

The invention may be described in the general context ofcomputer-executable instructions, such as program modules, beingexecuted by a computer. Generally, program modules include routines,programs, objects, components, data structures, etc. that performparticular tasks or implement particular abstract data types. Theinvention may also be practiced in distributed computing environmentswhere tasks are performed by remote processing devices that are linkedthrough a communications network. In a distributed computingenvironment, program modules may be located in both local and remotecomputer storage media including memory storage devices.

With reference to FIG. 1, an exemplary system for implementing theinvention includes a general purpose computing device in the form of acomputer 110. Components of computer 110 may include, but are notlimited to, a processing unit 120, a system memory 130, and a system bus121 that couples various system components including the system memoryto the processing unit 120. The system bus 121 may be any of severaltypes of bus structures including a memory bus or memory controller, aperipheral bus, and a local bus using any of a variety of busarchitectures. By way of example, and not limitation, such architecturesinclude Industry Standard Architecture (ISA) bus, Micro ChannelArchitecture (MCA) bus, Enhanced ISA (EISA) bus, Video ElectronicsStandards Association (VESA) local bus, and Peripheral ComponentInterconnect (PCI) bus also known as Mezzanine bus.

Computer 110 typically includes a variety of computer readable media.Computer readable media can be any available media that can be accessedby computer 110 and includes both volatile and nonvolatile media,removable and non-removable media. By way of example, and notlimitation, computer readable media may comprise computer storage mediaand communication media. Computer storage media includes both volatileand nonvolatile, removable and non-removable media implemented in anymethod or technology for storage of information such as computerreadable instructions, data structures, program modules or other data.Computer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by computer 110. Communication media typicallyembodies computer readable instructions, data structures, programmodules or other data in a modulated data signal such as a carrier waveor other transport mechanism and includes any information deliverymedia. The term “modulated data signal” means a signal that has one ormore of its characteristics set or changed in such a manner as to encodeinformation in the signal. By way of example, and not limitation,communication media includes wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared and other wireless media. Combinations of the any of the aboveshould also be included within the scope of computer readable media.

The system memory 130 includes computer storage media in the form ofvolatile and/or nonvolatile memory such as read only memory (ROM) 131and random access memory (RAM) 132. A basic input/output system 133(BIOS), containing the basic routines that help to transfer informationbetween elements within computer 110, such as during start-up, istypically stored in ROM 131. RAM 132 typically contains data and/orprogram modules that are immediately accessible to and/or presentlybeing operated on by processing unit 120. By way of example, and notlimitation, FIG. 1 illustrates operating system 134, applicationprograms 135, other program modules 136, and program data 137.

The computer 110 may also include other removable/non-removable,volatile/nonvolatile computer storage media. By way of example only,FIG. 1 illustrates a hard disk drive 141 that reads from or writes tonon-removable, nonvolatile magnetic media, a magnetic disk drive 151that reads from or writes to a removable, nonvolatile magnetic disk 152,and an optical disk drive 155 that reads from or writes to a removable,nonvolatile optical disk 156 such as a CD ROM or other optical media.Other removable/non-removable, volatile/nonvolatile computer storagemedia that can be used in the exemplary operating environment include,but are not limited to, magnetic tape cassettes, flash memory cards,digital versatile disks, digital video tape, solid state RAM, solidstate ROM, and the like. The hard disk drive 141 is typically connectedto the system bus 121 through a non-removable memory interface such asinterface 140, and magnetic disk drive 151 and optical disk drive 155are typically connected to the system bus 121 by a removable memoryinterface, such as interface 150.

The drives and their associated computer storage media discussed aboveand illustrated in FIG. 1, provide storage of computer readableinstructions, data structures, program modules and other data for thecomputer 110. In FIG. 1, for example, hard disk drive 141 is illustratedas storing operating system 144, application programs 145, other programmodules 146, and program data 147. Note that these components can eitherbe the same as or different from operating system 134, applicationprograms 135, other program modules 136, and program data 137. Operatingsystem 144, application programs 145, other program modules 146, andprogram data 147 are given different numbers here to illustrate that, ata minimum, they are different copies. A user may enter commands andinformation into the computer 110 through input devices such as akeyboard 162 and pointing device 161, commonly referred to as a mouse,trackball or touch pad. Other input devices (not shown) may include amicrophone, joystick, game pad, satellite dish, scanner, or the like.These and other input devices are often connected to the processing unit120 through a user input interface 160 that is coupled to the system bus121, but may be connected by other interface and bus structures, such asa parallel port, game port or a universal serial bus (USB). A monitor191 or other type of display device is also connected to the system bus121 via an interface, such as a video interface 190. In addition to themonitor, computers may also include other peripheral output devices suchas speakers 197 and printer 196, which may be connected through anoutput peripheral interface 195. Of particular significance to thepresent invention, a camera 163 (such as a digital/electronic still orvideo camera, or film/photographic scanner) capable of capturing asequence of images 164 can also be included as an input device to thepersonal computer 110. Further, while just one camera is depicted,multiple cameras could be included as an input device to the personalcomputer 110. The images 164 from the one or more cameras are input intothe computer 110 via an appropriate camera interface 165. This interface165 is connected to the system bus 121, thereby allowing the images tobe routed to and stored in the RAM 132, or one of the other data storagedevices associated with the computer 110. However, it is noted thatimage data can be input into the computer 110 from any of theaforementioned computer-readable media as well, without requiring theuse of the camera 163.

The computer 110 may operate in a networked environment using logicalconnections to one or more remote computers, such as a remote computer180. The remote computer 180 may be a personal computer, a server, arouter, a network PC, a peer device or other common network node, andtypically includes many or all of the elements described above relativeto the computer 110, although only a memory storage device 181 has beenillustrated in FIG. 1. The logical connections depicted in FIG. 1include a local area network (LAN) 171 and a wide area network (WAN)173, but may also include other networks. Such networking environmentsare commonplace in offices, enterprise-wide computer networks, intranetsand the Internet.

When used in a LAN networking environment, the computer 110 is connectedto the LAN 171 through a network interface or adapter 170. When used ina WAN networking environment, the computer 110 typically includes amodem 172 or other means for establishing communications over the WAN173, such as the Internet. The modem 172, which may be internal orexternal, may be connected to the system bus 121 via the user inputinterface 160, or other appropriate mechanism. In a networkedenvironment, program modules depicted relative to the computer 110, orportions thereof, may be stored in the remote memory storage device. Byway of example, and not limitation, FIG. 1 illustrates remoteapplication programs 185 as residing on memory device 181. It will beappreciated that the network connections shown are exemplary and othermeans of establishing a communications link between the computers may beused.

The exemplary operating environment having now been discussed, theremaining parts of this description section will be devoted to adescription of the program modules embodying the invention.

2.0 Real-Time Whiteboard Capture and Processing System and Method.

2.1 System Architecture.

Conceptually, the Real-time Whiteboard Capture System (RTWCS) consistsof three primary components: a capture device and interface 202, ananalysis/processing server 204, and browsing software 206, as shown inFIG. 2.

1. Capture Device and Capture Interface: The capture device of the RTWCSis used to capture images of the whiteboard content and to record theaudio associated with the creation of the whiteboard content. Thecapture unit is installed in a room where meetings take place. Itincludes a digital video camera, a microphone, and a personal computer(PC). The capture device takes video of the whiteboard and records audiovia the microphone that is stored to a PC. Both video and correspondingaudio are time stamped. The video and the audio samples are obtained ata common clock, usually the system clock. The timing of the common clockis associated with the video and audio samples and is stored as theirtime stamps.

2. Analysis server: The analysis server 204 is located in a centralplace and analyzes and stores the video and associated audio of thewhiteboard in real-time. It is thus available for later viewing. Likethe WCS, the analysis server of the RTWCS analysis server can calculatekey frames and index the recorded whiteboard images as described inco-pending U.S. patent application Ser. No. 10/178,443, entitled “ASYSTEM AND METHOD FOR WHITEBOARD AND AUDIO CAPTURE” filed on Jun. 19,2002. However, the RTWCS' analysis server 204 is also responsible forcontinually compressing, transmitting and synchronizing the whiteboardcontent and associated audio to remote meeting participants. In someembodiments of the invention, the analysis server 204 is alsoresponsible for capturing handwritten annotations on a printed documentin real-time.

3. Browsing software: The browsing software 206 allows the user to viewand play back the recorded and analyzed meeting data. The browsingsoftware 206 is preferably provided as a web plug-in to be installed bythe users who wish to view the meeting recordings.

2.2 Image Acquisition

The input to RTWCS is a sequence of video images, preferably in Bayerformat. Selected frames from an 82.5 second video input are shown inFIG. 3. It is necessary to analyze the video image sequence in order toseparate the whiteboard background from the person in the foreground andto extract the new pen strokes as they appear on the whiteboard.

As mentioned earlier, there are a number of advantages in using ahigh-resolution video camera over the sensing mechanism of devices likeMimio or an electronic whiteboard. However, the RTWCS has its own set ofunique technical challenges. For instance, the whiteboard backgroundcolor cannot be pre-calibrated (e.g., by taking a picture of the blankwhiteboard) because each indoor room has several light settings that mayvary from session to session and outdoor room lighting condition isinfluenced by the weather and the direction of the sun. In addition,people frequently move between the camera and the whiteboard, and theseforeground objects may occlude some portion of the whiteboard and castshadows on it. Within a sequence, there may be no single frame that iscompletely unoccluded. These problems need to be resolved in order toextract the new pen strokes.

2.2.1 Capture Device

Since the development of the initial Whiteboard Capture System (WCS),there have been tremendous advances in digital imaging hardware. Onenotable example is the availability of inexpensive high resolution videocameras and high-speed connections. For example, with an Aplux MU2 videocamera connected to any PC with a USB 2.0 port, one can capture 1.3 megapixel images at 7.5 Hz. The resolution of each video frame is 1280pixels by 1028 pixels—equivalent to 18 dpi for a 6′ by 4′ board. At 7.5Hz, the whiteboard content can be captured in near real time—good enoughto use in teleconferences. The RTWCS employs such a high resolutionvideo camera (e.g. in one embodiment an Aplux MU2). This provides aperfect compromise between the NTSC video camera and the high-resolutionstill image camera.

2.2.2 Capture Interface Requirements.

Like the initial WCS, the RTWCS does not require people to move out ofthe camera's field of view during capture as long as they do not blockthe same portion of the whiteboard during the whole meeting. Unlike WCS,the RTWCS does not need special installation or calibration. Sitting ona built-in stand, the video camera can be placed anywhere that has asteady and clear view of the whiteboard. It can be moved occasionallyduring the meeting. After each move, it will automatically and quicklyfind the whiteboard region again. In general, the RTWCS does this byconsidering everything in the field of view of the camera as possiblewhiteboard, computing the color for each cell corresponding to arectangular portion of the field of view and then fitting a plane acrossthe cells to identify any flat regions where the color changes smoothly.Once the planes are fit, outliers (colors that are different from theplane that is fitted) are discarded. Details of the process are providedin Section 2.3.3. This improvement has made the Real-time WhiteboardCapture System much more portable and easier to use than the WCS.Although the camera can be placed anywhere, the intended capture areashould occupy as much video frame as possible in order to maximize theavailable image resolution. For better image quality, it is also betterto place the camera right in front of the whiteboard in order to utilizethe depth-of-field of the lens to avoid out of focus.

2.2.3 Automatic Camera Exposure Adjustment.

The camera exposure parameter is preferably kept constant. If the lightsetting does not change, the color of whiteboard background should stayconstant in a sequence. In one embodiment of the invention, the RTWCScontains a module that automatically sets the exposure to minimize thenumber of saturated pixels (i.e. brightness level is 0 or 255). Thismodule is run once when the system is started and triggered to run againwhenever a global change in cell color is detected (see Section 2.3.2).

2.3 Image Sequence Analysis

Since the person who is writing on the board is in the line of sightbetween the camera and the whiteboard, he/she often occludes some partof the whiteboard. It is thus necessary to segment the images intoforeground objects and whiteboard. For that, two primary heuristics arerelied on: 1) Since the camera and the whiteboard are stationary, thewhiteboard background cells are stationary throughout the sequence untilthe camera is moved; 2) Although sometimes foreground objects (e.g., aperson standing in front of the whiteboard) occlude the whiteboard, thepixels that belong to the whiteboard background are typically themajority. The RTWCS exploits these heuristics extensively.

The RTWCS applies several strategies to make it efficient enough to runin real time.

First, rather than analyzing the images at pixel level, the RTWCSdivides each video frame into rectangular cells to lower thecomputational cost. The cell size is preferably roughly the same as whatone expects the size of a single character on the board (16 by 16 pixelsin one embodiment of the RTWCS). The cell grid divides each frame in theinput sequence into individual cell images, which are the basic unit inthe RTWCS's analysis.

Second, the RTWCS's analyzer is structured as a pipeline of six analysismodules (see FIG. 4A). If a cell image does not meet the condition in aparticular module, it will not be further processed by the subsequentmodules in the pipeline. Therefore, many cell images do not go throughall six modules. At the end, only a small number of cell imagescontaining the newly appeared pen strokes come out of the analyzer. Thesix modules are:

-   -   1. Change detector 402: This module determines if the cell        images have changed since last frame.    -   2. Color estimator 404: This module computes the background        color of the cell images—the color of blank whiteboard.    -   3. Background modeler 406: This is a dynamic module that updates        the whiteboard background model by integrating the results        computed from the previous modules which may have missing parts        due to occlusion by foreground objects.    -   4. Cell classifier 408: This module classifies the cell images        into foreground or whiteboard cells.    -   5. Stroke extractor 410: This module extracts the newly appeared        strokes.    -   6. Color enhancer 412: The color enhancer enhances the color of        the newly appeared extracted strokes.        The change detector, color estimator, background modeler and        cell classifer modules all preferably operate one image data in        Bayer color space.

A flowchart depicting the processing of one embodiment of the inventionis shown in FIG. 5. An image frame cell in Bayer format is input intothe RTWCS, as shown in process action 502. This image frame cell iscompared to the previous image in the corresponding cell location in theprevious frame (process action 504). It is determined if the cell imageshave changed (process action 506), preferably using a NormalizedCross-correlation algorithm, and if so, the cell age is set to 1(process action 508). If it is determined that the cell image has notchanged (process action 506), the cell age is incremented (processaction 510). An assessment is then made as to whether its age is greaterthan a given age threshold (process action 512). If not, the cell frameimage is not processed any further. If the cell age is greater than theage threshold, then the whiteboard background is computed for this cellframe image and then this cell frame image is used to update thewhiteboard color model (process actions 516). The cell image is thenclassified as either a foreground or whiteboard cell, as shown inprocess action 518. Any cells classified as foreground are no longerprocessed (process action 520). If the cell frame image is a whiteboardcell, any newly appeared strokes are output (process action 522).Optionally, the color of the newly extracted strokes can be enhanced(process action 524) and stored and/or transmitted to one or more remotemeeting participants (process action 526). This process is repeated foreach image frame cell in the frame for each sequential frame.

The third strategy employed to let the RTWCS operate in real-time isspecific to the video camera that is used in the RTWCS. The video cameraused (e.g., an Aplux MU2) allows the video frames to be directlyaccessed in Bayer format, which is the single channel raw image capturedby the CMOS sensor. In general, a demosaicing algorithm is run on theraw image to produce an RGB color image. By processing the cell imagesin raw Bayer space instead of RGB space and delaying demosaicing untilthe final step and running it only on the cells containing new strokes,the RTWCS saves memory and processing by at least 66%. An additionalbenefit is that one can obtain a higher quality RGB image at the end byusing a more sophisticated demosaicing algorithm than the one built intothe camera driver.

An overview of the RTWCS having been provided, the following paragraphsprovide the details of the system and method according to the presentinvention.

2.3.1 Analysis State

The RTWCS keeps track of some states as it processes the input videoframes. These include: 1) The last video frame it has processed; 2) Theage of each cell image in the last frame. The age is defined to be thenumber of frames that the cell image remains unchanged; 3) Cell imageswith whiteboard content that have been detected so far; 4) Thewhiteboard background model (see Section 2.3.4 for details).

2.3.2 Assigning Age to Cells and Determining Cell Image Change.

The RTWCS first assigns an age to each cell image. To determine whethera cell image has changed, it is compared against the image of the samecell (e.g., the cell in the same location) in the previous frame using amodified Normalized Cross-Correlation (NCC) algorithm. Note that the NCCis applied to the images in the Bayer space.

Consider two cell images I and I′. Let Ī and Ī′ be their mean colors andσ and σ′ be their standard deviations. The normalized cross-correlationscore is given by

$c = {\frac{1}{N\;{\sigma\sigma}^{\prime}}{\sum\limits_{i}{\left( {I_{i} - \overset{\_}{I}} \right)\left( {I_{i}^{\prime} - {\overset{\_}{I}}^{\prime}} \right)}}}$where the summation is over every pixel i and N is the total number ofpixels. The score ranges from −1, for two images not similar at all, to1, for two identical images. Since this score is computed after thesubtraction of the mean color, it may still give a high value even twoimages have very different mean colors. So one has an additional test onthe mean color difference based on the Mahalanobis distance [3], whichis given by d=|Ī−Ī′|/√{square root over (σ²+σ^(′2))}. In summary, twocell images I and I′ are considered to be identical and thus should beput into the same group if and only if d<T_(d) and c>T_(c). In oneembodiment of the invention, T_(d)=2 and T_(c)=0.707.

If the comparison indicates a cell image is changed from the previousframe, its age is set to 1. Otherwise, it is incremented by 1. At eachframe, all the cells that have been stationary for more than the agethreshold (4 frames in one embodiment of the RTWCS—about 0.5 second at7.5 Hz) are considered to be the background candidates and fed to theWhiteboard Color Model Update module. If the age is not greater than theage threshold, the cell image is not processed further during thisframe. The age threshold is a trade-off between the output delay andanalysis accuracy.

The RTWCS also computes the percentage of the cell images that havechanged. If the change is more than 90%, the RTWCS assumes thatsomething drastic and global has happened since the last frame (e.g.light setting is changed, camera is moved, etc.). In such an event, allstates are re-initialized and an exposure calibration routine is called.Other more localized changes (e.g. people moving across, gradual changein sun light) are handled dynamically by the Whiteboard Color ModelUpdate Module. More specifically, as shown in FIG. 6, the systeminitializes the states of the variables of the system (e.g., cell age,last video frame, cell images with whiteboard content detected so far,whiteboard background model), and the cells of a given frame areanalyzed to see if they have changed (process action 604). If more thana prescribed number of cells have changed (e.g., more than 90% in oneembodiment), there is a high probability that the lighting condition haschanged (e.g., a light is turned on or off); in consequence, an exposurecalibration routine is called (process action 606) (see below) and thestates are reinitialized (process action 602).

The exposure calibration procedure (process action 606) works asfollows. If a camera allows the exposure parameter to be controlled bysoftware, the exposure calibration procedure automatically sets theappropriate amount of light to be captured to form each video frame inorder to avoid over or under exposing the resulting images. Thisembodiment uses a binary search algorithm for an exposure setting. TheRTWCS initializes the lower and upper limits of the search range to bethe entire range of the exposure setting (e.g. 0 and 255 respectivelyfor 8 bits cameras). To start with, the exposure is set to be theaverage of the lower and upper limits. For each incoming image,measurements of over-exposure and under-exposure are taken. For example,a histogram of pixel intensity can be constructed. If over 1% of pixelstake the value 255, then it is over-exposed. If over 1% of pixels takethe value 0, then it is under-expose. If it is neither over-exposed norunder-exposed, then the procedure is completed. If it is both over- andunder-exposed, then the procedure is aborted since the dynamic range ofthe scene exceeds that of the camera sensor. If it is over exposed, thenthe upper searching limit is set to be the current exposure setting. Ifit is under exposed, then the lower searching limit is set to be thecurrent exposure setting. In either of the latter cases, the currentexposure is set to be the average of the updated lower and upper limits.The procedure is repeated until it completes.

If a camera has more than one parameter controllable by the software(e.g., both exposure and contrast), one can design a more flexiblecalibration procedure such that both over- and under-exposures areavoided.

2.3.3 Computing the Background Color.

To classify cells, the RTWCS needs to know for each cell what thewhiteboard background color is (i.e. the color of the whiteboard itselfwithout anything written on it). The whiteboard background color is alsoused in color-enhancing the extracted cell images, so it needs to beestimated accurately to ensure the quality.

Since the ink absorbs the incident light, the luminance of thewhiteboard pixels is higher than pen stroke pixels. The whiteboard colorwithin the cell is therefore the color with the highest luminance. Inpractice, the colors of the pixels in the top 10th percentile areaveraged in order to reduce the error introduced by sensor noise. Hence,the color of each cell is computed by first sorting the pixels of thesame color channel (128 green, 64 blue and 64 red values in a 16×16 cellimage in Bayer space) and then taking the values of top 10% percentilein each channel.

More specifically, as shown in FIG. 7, the image of the whiteboard isdivided into rectangular cells (process action 702), and the pixels ineach cell of the same color channel (preferably in Bayer color space)are sorted according to their luminance values (process action 704). Thevalues of the pixels in the top 10% is averaged and assigned as the cellcolor for that channel (process action 706). The cells can be filteredby locally fitting a plane in Bayer space, rejecting outliers andreplacing these outliers by the interpolated values of neighboring cellsfor each color channel (process action 708).

2.3.4 Updating the Whiteboard Color Model.

The color computed from the previous section will give good estimationof whiteboard color for the cells containing some whiteboard background.Though, it will give the wrong color when the cells contain only theforeground or pen strokes (first image in FIG. 8). The RTWCS has toidentify those cells to prevent them from contaminating the whiteboardcolor model.

The RTWCS uses a least-media-squares algorithm, which fits a globalplane over the colors and throws away the cells that contain outliercolors (see Appendix for details). The remaining cells are considered asbackground cells and their colors are used to update the whiteboardbackground (second image in FIG. 8).

The RTWCS then uses a Kalman filter to dynamically incorporate thebackground colors computed from the current frame into the existingwhiteboard background color model. The state for the cell i is its colorC_(i), together with variance P_(i) representing the uncertainty. P_(i)is initially set to ∞ to indicate no observation is available. Theupdate is done in two steps:

-   1) Integrate. Let O_(i) be the color of cell i computed from the    current frame. There is also an uncertainty, Q_(i), associated with    O_(i). In one embodiment of the RTWCS, it can only be one of two    values: ∞ if the cell color is an outlier, 4 otherwise (i.e., the    standard deviation is equal to 2 intensity levels). Considering    possible lighting variation during the time elapsed since the last    frame, the uncertainty P_(i) is first increased by Δ (4 in one    embodiment of the system, equivalent to a standard deviation of 2).    C_(i) and P_(i) are then updated according to the classic Kalman    filter formula:

$K = \frac{P_{i}}{P_{i} + Q_{i}}$ C_(i) = C_(i) + K ⋅ (O_(i) − C_(i))P_(i) = (1 − K) ⋅ P_(i)

-   2) Propagate. In order to fill the holes created by the cells that    are occluded by foreground objects and to ensure the color model is    smooth, the cell colors are propagated to the neighboring cells. For    each cell i, it incorporates the 4 of its neighbors' states    according to the following:

$C_{i} = \frac{{C_{i}P_{i}^{- 1}} + {\frac{1}{16}{\sum\limits_{j}{C_{j}\left( {P_{j} + \Delta} \right)}^{- 1}}}}{P_{i}^{- 1} + {\frac{1}{16}{\sum\limits_{j}\left( {P_{j} + \Delta} \right)^{- 1}}}}$$P_{i} = \left( {P_{i}^{- 1} + {\frac{1}{16}{\sum\limits_{j}\left( {P_{j} + \Delta} \right)^{- 1}}}} \right)^{- 1}$Note that one increases the uncertainty of its neighbors by Δ (4 in oursystem) to allow color variation. A hole of size N generally takes N/2frames to get filled. Since the uncertainty in the cells with filledvalues is much larger than the ones with the observed values (due toadded Δ), the filled values are quickly supplanted by the observedvalues once they become available. An example of an integratedwhiteboard color is the third image in FIG. 8C. Note that the bookshelfarea in the left side of the image is never filled.

2.3.5 Classifying Cells.

This module determines whether a cell image is a foreground object orthe whiteboard. In the cell classifying process, the cell image beingoperated on converts the cell image from Bayer color space to RGB colorspace on a cell basis, vice pixel basis. The RTWCS performs this in twolevels: individual and neighborhood.

As shown in FIG. 9, at the individual cell level, given a goodwhiteboard color model, the RTWCS simply computes the Euclidean distancebetween the background color of the cell image (computed in Section2.3.2) and the color of the corresponding cell location in thewhiteboard background model (process action 902). If the differenceexceeds a threshold (there are four brightness levels in one embodimentthe system), the cell image is classified as a foreground object(process actions 904, 906).

However, more accurate results can be achieved by utilizing spatialrelationship among the cell groups. The basic observation is thatforeground cells should not appear isolated spatially since a personusually blocks a continuous region of the whiteboard. So at theneighborhood level, the RTWCS performs two filtering operations on everyframe. First, the RTWCS identifies isolated foreground cells andreclassifies them as whiteboard cells (process actions 908, 910). Thisoperation corrects the misclassification of the cells that are entirelyfilled with strokes. Second, the RTWCS reclassifies whiteboard cellswhich are immediately connected to some foreground cells as foregroundcells (process action 912). One main purpose of the second operation isto handle the cells at the boundaries of the foreground object. Noticethat if such a cell contains strokes, the second operation wouldincorrectly classify this cell as a foreground object. It will becorrectly re-classified as whiteboard once the foreground object movesaway. Extending the foreground object boundary delays the recognition ofstrokes by a few frames, but it prevents some parts of the foregroundobject from being classified as strokes—a far worse situation. FIG. 10provides samples of classification result.

2.3.8 Extracting New Strokes

The cells classified as foreground are not further processed. For cellsclassified as whiteboard, the RTWCS checks whether there is a whiteboardcell already existing in the same cell location in the output depository(process action 916). If not, the cell is a new whiteboard cell, asshown in process action 918. If a whiteboard cell does exist, the RTWCSstill needs to check whether the existing cell and the current cellimage are the same, using the same image difference algorithm in Section2.3.2 (process action 920). If they are different, the user probably haserased the whiteboard and/or written something new, and therefore thewhiteboard cell in the output depository is replaced by the current cellimage (process action 922). Periodically (every 30 seconds in oneembodiment), the RTWCS updates all existing whiteboard cells withcurrent cell images to account for possible lighting variations.

2.3.9 Color-Enhancing the Stroke Images.

At this stage, the newly extracted cell images are finally convertedfrom raw Bayer images into RGB images. Any conventional demosaicingalgorithm can be used. For example, the RTWCS can use a demosaicingalgorithm proposed by Laroche-Prescott [5]. Alternately, for example,the RTWCS could employ a demosaicing algorithm which is the subject of aco-pending patent application entitled “HIGH-QUALITY GRADIENT-CORRECTEDLINEAR INTERPOLATION FOR DEMOSAICING OF COLOR IMAGES” filed on Mar. 15,2004.

After demosaicing, the images still look color-shifted and noisy. Theyneed to be white-balanced and color-enhanced, which also helps when theimages are printed and compressed. The process consists of two steps:

-   1. Make the background uniformly white and increase color saturation    of the pen strokes. For each cell, the whiteboard color computed in    Section 2.3.2, Ī_(w), is used to scale the color of each pixel in    the cell:

$I_{out} = {{\min\left( {255,{\frac{I_{in}}{{\overset{\_}{I}}_{w}} \cdot 255}} \right)}.}$

-   2. Reduce image noise. The RTWCS remaps the value of each color    channel of each pixel in the output frames according to an S-shaped    curve.-   FIG. 4B is an example of such color enhancement.    3.0 Teleconferencing Experience.

To test the RTWCS in a real teleconference setting, in one embodiment ofthe RTWCS the system was adapted to be a plug-in to a Whiteboard appletof the Microsoft Windows Messenger. The Whiteboard applet allows theusers at two ends of a Windows Messenger session to share a digitalwhiteboard. The user at one end can paste images or draw geometricshapes and the user at the other end can see the same change almostinstantaneously. Usually, the user draws objects with his mouse, whichis very cumbersome. With the RTWCS, the user can write on a realwhiteboard instead.

The RTWCS in the tested embodiment takes 45–50% processing power of a2.4 G Hz Pentium 4 PC. Once launched, the RTWCS initializes in about 5seconds, which includes the time to do exposure calibration, initializethe whiteboard color model, and capture the content already existing onthe whiteboard.

The changes to the whiteboard content are automatically detected by theRTWCS and incrementally piped to the Whiteboard applet as small cellimage blocks. The Whiteboard applet is responsible for compressing andsynchronizing the digital whiteboard content shared with the remotemeeting participant. The remote participant can add annotations on topof the whiteboard image using the mouse. When used with other WindowsMessenger tools, such as voice conferencing and application sharing,whiteboard sharing becomes a very useful tool in communicating ideas.

The time delay between the appearance of the stroke in input video andshowing up on the local Whiteboard applet is 1 second. Network transporttakes additional 0.5 second or more depending on the distance betweenthe two ends. Because the resulting image contains only uniformbackground and a handful of colors, the required communication bandwidthafter compression is proportion to the amount of content that the userproduces. Using GIF compression, a reasonably full whiteboard image at1.3 MP takes about 200 K bytes (FIG. 4B takes 70 K bytes). After theinitial image capture, the whiteboard updates take 50–100 bytes percell. Since usually only a handful of cells are changing at a time whenthe whiteboard is in use, the sustained network bandwidth requirement isfar below those of video conferencing solutions—suitable even for use ina dial-up network.

4.0 Capturing Printed Documents and Annotations.

The RTWCS can also be used to capture handwriting annotations on printeddocuments in real time—a common scenario in teleconferencing whenparticipants need to share paper documents. A gooseneck support can beemployed so the camera can be pointed downward securely. When capturing8.5″×11″ sized documents, it was found that the document image islegible down to 6 point fonts.

To overcome the problem of small movements of the paper when it is beingwritten on, an efficient homography-based image matching algorithm wasadded to align each input video frame to first frame—equivalent tomotion stabilizing the input video. This modification removes most ofthe resets related to paper movement and makes the system much moreusable.

The foregoing description of the invention has been presented for thepurposes of illustration and description. It is not intended to beexhaustive or to limit the invention to the precise form disclosed. Manymodifications and variations are possible in light of the aboveteaching. It is intended that the scope of the invention be limited notby this detailed description, but rather by the claims appended hereto.

Appendix: Plane-Based Whiteboard Color Estimation

Only one component of the color image is considered, but the techniquedescribed below applies to all components (R, G, B, or Y). Each cell iis defined by its image coordinates (x_(i), y_(i)). Its color isdesignated by z_(i) (z=R, G, B, or Y). The color is computed asdescribed in Section 2.3.2, and is therefore noisy and even erroneous.From experience with the meeting rooms, the color of the whiteboardvaries regularly. It is usually much brighter in the upper part andbecomes darker toward the lower part, or is much brighter in one of theupper corners and becomes darker toward the opposite lower corner. Thisis because the lights are installed against the ceiling. Therefore, fora local region (e.g., 7×7 cells), the color can be fit accurately by aplane; for the whole image, a plane fitting is still very reasonable,and provides a robust indication whether a cell color is an outlier.

A plane can be represented by ax+by+c−z=0. A set of 3D points{(x_(i),y_(i),z_(i))|i=1, . . . , n} with noise only in z_(i) is given.The plane parameters p=[a, b, c]^(T) can be estimated by minimizing thefollowing objective function: F=Σ_(i)ƒ_(i) ², whereƒ_(i)=ax_(i)+by_(i)+c−z_(i). The least-squares solution is given byp=(A^(T)A)⁻¹ A^(T) z, where

$A = \begin{bmatrix}x_{1} & y_{1} & 1 \\\cdots & \cdots & \cdots \\x_{n} & y_{n} & 1\end{bmatrix}$and z=[z₁, . . . z_(n)]^(T). Once the plane parameters are determined,the color of the cell i is replaced by {circumflex over(z)}_(i)=ax_(i)+by_(i)+c.

The least-squares technique is not robust to erroneous data (outliers).As mentioned earlier, the whiteboard color initially computed doescontain outliers. In order to detect and reject outliers, a robusttechnique to fit a plane to the whole whiteboard image is used. Theleast-median-squares [11], a very robust technique that is able totolerate near half of the data to be outliers, is used. The idea is toestimate the parameters by minimizing the median, rather than the sum,of the squared errors, i.e.,

$\min\limits_{p}{\underset{i}{median}{f_{i}^{2}.}}$First m random subsamples of 3 points are drawn (3 is the minimum numberto define a plane). Each sub-sample gives an estimate of the plane. Thenumber m should be large enough such that the probability that at leastone of the m sub-samples is good is close to 1, say 99%. If it isassumed that half of the data could be outliers, then m=35, thereforethe random sampling can be done very efficiently. For each sub-sample,the plane parameters and the median of the squared errors ƒ_(i) ² arecomputed. The plane parameters that give the minimum median of thesquared errors were retained, denoted by M. Then the so-called robuststandard deviation σ=1.4826√{square root over (M)} (the coefficient isused to achieve the same efficiency when no outliers are present) iscomputed. A point i is considered to be an outlier and discarded if itserror |ƒ_(i)|>2.5 σ. Finally, a plane is fit to the good points usingthe least-squares technique described earlier. The color of an outliercell i is replaced by {circumflex over (z)}_(i)=ax_(i)+by_(i)+c.

REFERENCES

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1. A computer-implemented process for outputting whiteboard content,comprising the following process actions: inputting a sequence of imageframes of content written on a whiteboard in real-time; dividing each ofsaid image frames into cells; for each cell in each image frame,determining if there is a change in the cell image compared to acorrespondingly located cell the immediately preceding image frame insaid sequence of image frames; whenever it is determined that there is acolor change, setting a cell age to a prescribed minimum value, and ifthere is no change increasing cell age by a prescribed increment value;determining if cell age is greater than a specified threshold value;whenever the cell age is not greater than the threshold value, notprocessing said cell any further; if said cell age is greater than thethreshold value, computing the background color of the cell; updating awhiteboard color model; classifying each cell image as a foreground orwhiteboard cell using said whiteboard color model; whenever a cell imageis classified as a foreground cell not processing that cell any further;and whenever a cell image is classified as a whiteboard cell, outputtingthe cell image whenever it exhibits strokes not present in thecorrespondingly located cell in the preceding image frames or is missingstrokes found in the preceding image frames in real-time.
 2. Thecomputer-implemented process of claim 1 wherein the image frames areinput in Bayer format.
 3. The computer-implemented process of claim 1further comprising the process action of outputting the image framecells so that the strokes on the whiteboard are displayed in an enhancedmanner using said cell classification.
 4. The computer-implementedprocess of claim 3 wherein the process action of outputting the framecells so that the strokes on the whiteboard are displayed in an enhancedmanner comprises the process actions of: making the whiteboardbackground color for cells displaying whiteboard background in saidsequence of image frames more uniform; increasing the stroke saturationof the whiteboard content to make the strokes more vivid and legible;and reducing image noise in said sequence of image frames.
 5. Thecomputer-implemented process of claim 4 wherein the process action ofoutputting a cell comprises outputting only changed whiteboard cells. 6.The computer-implemented process of claim 1, wherein said cellclassifying process action comprises: comparing the input image framecell to a previous image in a corresponding cell location by comparingthe difference between the background color of the cell image and thecorresponding color of the whiteboard model; if the difference exceeds agiven threshold, designating the cell as a foreground cell, and if thedifference does not exceed a given threshold designating the cell as awhiteboard cell; determining if the cell location was a whiteboard cellin a previous output, and if not designating the cell as a newwhiteboard cell and outputting its cell image; if the cell location wasnot a whiteboard cell in the previous output, determining if the cellimage in the same location of the output is the same and if notoutputting its cell image.
 7. The computer-implemented process of claim6 wherein the difference between a background color of the cell imageand the corresponding color in the whiteboard color model is determinedby determining the Euclidean distance between the cell image and thecorresponding cell location in the whiteboard color model.
 8. Thecomputer-implemented process of claim 6 further comprising the processaction of verifying that said cell classified as a whiteboard cell is awhiteboard cell, comprising the process actions of: determining whethersaid whiteboard cell is connected to any foreground cells; and if saidwhiteboard cell is determined to be connected to any foreground cells,classifying it as a foreground cell.
 9. The computer-implemented processof claim 6 further comprising the process action of verifying that saidcell classified as a foreground cell is a foreground cell, comprisingthe process actions of: determining whether said foreground cell isconnected to other foreground cells; and if a cell is determined to be aforeground cell and not connected to other cells, classifying it as awhiteboard cell.
 10. The computer-implemented process of claim 1 whereinthe process action of updating the whiteboard color model for thewhiteboard and stroke cells comprises the process actions of: dividingthe image of the whiteboard into cells; sorting the pixels in each cellaccording to their luminance value; and assigning the highest luminancevalue in each cell as the resulting whiteboard color of that cell. 11.The computer-implemented process of claim 10 further comprising theprocess action of filtering the colors of the cells comprising thefollowing process actions: subjecting the resulting cell colors to aleast-median-square error procedure, which fits a global plane over thecolors and throws away the cells that are foreground cells; designatingthe remaining cells as whiteboard background cells; using said remainingwhiteboard background cells to update the whiteboard background; andfilling the holes in the whiteboard color model created by throwing outthe cells that were foreground cells with known colors from neighboringcells with whiteboard background colors.
 12. The computer-implementedprocess of claim 1 wherein the process action of updating the whiteboardcolor model comprises the process actions of: dividing the image of thewhiteboard into cells; sorting the pixels in each cell according totheir luminance value; and averaging the top 10% values of the luminancevalues in each cell and assigning this value as the resulting whiteboardcolor of that cell.
 13. The computer-implemented process of claim 1further comprising correlating said audio recordings with said video.14. The computer-implemented process of claim 1 wherein determining ifthere is a change in said cell image is computed in Bayer color space.15. The computer-implemented process of claim 1 wherein the backgroundcolor of the cell is computed in Bayer color space.
 16. Thecomputer-implemented process of claim 1 wherein the whiteboard colormodel is computed in Bayer color space.
 17. The computer-implementedprocess of claim 1 wherein classifying cell images is computed in Bayercolor space.
 18. The computer-implemented process of claim 1 whereindetermining if cell images are the same comprises using a normalizedcross-correlation technique to compare two cells at a time.
 19. Thecomputer-implemented process of claim 18 wherein the cross-correlationscore ranges from −1, for two images not similar at all, to 1, for twoidentical images.
 20. The computer-implemented process of claim 19further comprising a process action of applying a Mahalanobis distancetest to determine if two cells are the same.
 21. Thecomputer-implemented process of claim 20 wherein the Mahalanobisdistance test given by d=|Ī−Ī′|/(σ+σ′); where I is the first cell imageand I′ is the second cell image, Ī is the mean color of the first cellimage, Ī′, is the mean color of the second cell image, σ is the standarddeviation from Ī and σ′ is the standard deviation from Ī′ and wherein Iand I′ are the two cell images are considered to be identical if andonly if d<T_(d) and C>T_(c), and wherein T_(d)=2 and T_(c)=0.707. 22.The computer-implemented process of claim 1 wherein the whiteboard colormodel is dynamically updated by incorporating the background color fromthe current frame into the existing whiteboard color model.
 23. Thecomputer-implemented process of claim 22 wherein the background colorfrom the whiteboard color model is updated using a Kalman filtertechnique.
 24. The computer-implemented process of claim 1 furthercomprising the process action of analyzing the sequence of images toisolate key frames summarizing key points of said whiteboard content.25. A computer-readable medium having computer-executable instructionsfor transferring whiteboard content captured during a meeting inreal-time, said computer executable instructions comprising programmodules for: capturing a sequence of images of content written on anon-electronic whiteboard in Bayer color format; recording audio signalscorrelated with the sequence of images; and analyzing the sequence ofimages to transmit said images of the whiteboard and audio signals to atleast one remote meeting participant by: dividing each of said imageframes into cells of cell images; determining if there is a change inthe cell image compared to a correspondingly located cell theimmediately preceding image frame in said sequence of image frames;whenever it is determined that there is a color change, setting a cellage to a prescribed minimum value, and if there is no change increasingcell age by a prescribed increment value; determining if cell age isgreater than a specified threshold value; whenever the cell age is notgreater than the threshold value, not processing said cell any further;if said cell age is greater than the threshold value, computing thebackground color of the cell; updating a whiteboard color model;classifying each cell image as a foreground or whiteboard cell usingsaid whiteboard color model; whenever a cell image is classified as aforeground cell not processing that cell any further; and whenever acell image is classified as a whiteboard cell, outputting the cell imagewhenever it exhibits strokes not present in the correspondingly locatedcell in the preceding image frames or is missing strokes found in thepreceding image frames in real-time.
 26. The computer-readable medium ofclaim 25 further comprising a module for correcting for exposure if morethan a prescribed percentage of the cells have changed.
 27. Thecomputer-readable medium of claim 26 wherein the prescribed percentageof cells is 90 percent.
 28. The computer-readable medium of claim 26wherein the module for correcting for exposure automatically sets theappropriate amount of light to be captured to form each image frame inorder to avoid over or underexposing resulting image frame.
 29. Thecomputer-readable medium of claim 26 wherein the module for correctingfor exposure comprises sub-modules for: initializing the lower and upperlimits of a search range of a search algorithm to be the entire range ofthe exposure setting; setting the search range to the average of thelower and upper limits; measuring over-exposure and under-exposure foreach incoming image frame; exiting the module for correcting exposure ifa given incoming image frame is neither over-exposed or under-exposed;if an incoming image frame is over exposed, setting the upper searchinglimit to the current exposure setting; if an incoming image frame isunder exposed, setting the lower searching limit to the current exposuresetting; and if an incoming image frame is either over exposed or underexposed, setting the exposure of the incoming image frame to the averageof the updated lower and upper limits.
 30. A computer-implementedprocess for creating an enhanced data stream content written on a pieceof paper, comprising the following process actions: inputting a sequenceof image frames of content written on the document in real-time;dividing each of said image frames into cells of cell images; for eachcell location in each image frame, determining if there is a change insaid cell image from the cell in the same location in the previous imageframe in said sequence of image frames; if there is a change, settingcell age to a minimum value, and if there is no changes increasing cellage by a prescribed value; determining if cell age is greater than aspecified value; if cell age is not greater than a specified value, notprocessing said cell further for said image frame; if said cell age isgreater than a specified value, computing the background color of thecell; updating a document color model; classifying cell images intoforeground or document cells using said document color model; if a cellimage is classified as a foreground cell not processing that cell anyfurther for that input image frame; and if a cell image is notclassified as a foreground cell, outputting any cell image frames withnewly appeared strokes or newly deleted strokes in real-time.